Platoon Control Method for Connected and Automated Vehicles Based on Physics‐Informed Reinforcement Learning

ABSTRACT

Connected and automated vehicle (CAV) platoon control technologies have demonstrated significant potential in improving traffic efficiency, ensuring safety and reducing energy consumption. However, uncertainties in the driving environment can easily lead to control failures. Reinforcement learning (RL), with its strong learning and imitation capabilities, has been widely applied to CAV platoon control, yet it suffers from poor interpretability and output instability, posing safety risks. This study proposes a CAV platoon control method that integrates RL with physical information. A Twin Delayed Deep Deterministic Policy Gradient (TD3)‐based platoon control model is developed. The design explicitly considers traffic efficiency, driving comfort and platoon stability. Physical rules are incorporated as an additional loss term into the policy network of this model, jointly guiding the policy iteration process of the actor network, thereby improving training efficiency and interpretability. The feasibility and effectiveness of the proposed method are validated under both low and high disturbance scenarios. The results indicate that, compared with physics‐based and Model Predictive Control‐based (MPC‐based) methods, the proposed method achieves more balanced control under both disturbance conditions, with overall performance improvements of approximately 7.9% and 5.9%, respectively. The proposed method demonstrates excellent performance in vibration suppression, energy‐efficient driving and generalisation capabilities, providing an effective technical solution for intelligent decision‐making in complex driving environments.